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A Floating-Gate Photoelectric Synaptic Transistor Utilizing BP/POx/WSe2 Heterostructure for Neuromorphic Visual

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  • 1School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, 430074, China.

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Researchers developed a novel two-dimensional van der Waals transistor for artificial intelligence (AI) hardware. This neuromorphic device mimics human visual memory with ultralow energy consumption, enabling efficient edge AI applications.

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2D vdW materialsfloating‐gate transistorneuromorphic computingphotoelectronic synaptic device

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Area of Science:

  • Materials Science
  • Neuromorphic Engineering
  • Artificial Intelligence

Background:

  • Traditional AI hardware faces limitations in energy efficiency and scalability due to von Neumann architectures and silicon-based synapses.
  • Two-dimensional (2D) van der Waals (vdW) materials offer unique properties like atomic thickness and tunable electronics for advanced hardware.
  • Developing efficient synaptic devices is crucial for next-generation neuromorphic computing.

Purpose of the Study:

  • To engineer a 2D van der Waals (vdW) heterostructure transistor for efficient neuromorphic hardware.
  • To demonstrate synaptic functionalities and human visual memory mimicry in the device.
  • To integrate the device into a convolutional neural network (CNN) for advanced AI tasks.

Main Methods:

  • Fabrication of a vdW floating-gate transistor using Black Phosphorus (BP) and Tungsten Diselenide (WSe2) with a P-oxide (POx) insulator.
  • Characterization of the transistor's electrical properties, including on-off current ratio and memory window.
  • Demonstration of key synaptic plasticity behaviors (STP, LTP, PPF, LTP/D) and visual memory emulation.
  • Implementation of a two-path CNN utilizing both optical and electronic inputs for face recognition.

Main Results:

  • The vdW transistor achieved a high on-off current ratio (≈10^5) and a large memory window (73 V).
  • The device successfully mimicked human visual memory under optical stimuli with ultralow energy consumption (10 pJ/event).
  • A CNN incorporating the device achieved 96.9% accuracy on the Labeled Faces in the Wild (LFW) dataset.
  • The heterostructure engineering optimized interface band alignment for enhanced performance.

Conclusions:

  • The developed vdW transistor represents a significant advancement in neuromorphic hardware, overcoming limitations of current AI architectures.
  • This technology enables energy-efficient, human-like visual processing for edge AI applications.
  • The synergistic integration of optical and electronic inputs in the CNN showcases a promising direction for future AI systems.